Lightweight Convolutional Neural Network and Its Application in Rolling Bearing Fault Diagnosis under Variable Working Conditions

Liu, Hengchang; Yao, Dechen; Yang, Jianwei; Li, Xi

doi:10.3390/s19224827

Cited by 46 publications

(34 citation statements)

References 35 publications

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“…Considering the accuracy and running speed, we constructed our AI model based on the ShuffleNet V2 network [26, 27]. The CNN model using ShuffleNet V2 as the backbone is the most advanced lightweight model available, which has better accuracy and run faster than previous lightweight networks with the same computation condition.…”

Section: Methodsmentioning

confidence: 99%

Automated Diagnosis of COVID-19 Using Deep Learning and Data Augmentation on Chest CT

Ruan

Xiang

et al. 2020

Preprint

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Background Coronavirus disease 2019 has surprised the world since the beginning of 2020, and the rapid growth of COVID-19 is beyond the capability of doctors and hospitals that could deal in many areas. The chest computed tomography (CT) could be served as an effective tool in detection of COVID-19. It is valuable to develop automatic detection of COVID-19. Materials and MethodsThe collected dataset consisted of 1042 chest CT images (including 521 COVID-19, 397 healthy, 76 bacterial pneumonia and 48 SARS) obtained by exhaustively searching available data on the Internet. Then, these data are divided into three sets, referred to training set, validation set and testing set. Sixteen data augmentation operations are designed to enrich the training set in deep learning training phase. Multiple experiments were conducted to analyze the performance of the model in the detection of COVID-19 both in case of no noisy labels and noisy labels. The performance was assessed by the area under the receiver operating characteristic (AUC), sensitivity, specificity and accuracy. Results The data augmentation operations on the training set are effective for improvement of the model performance. The area under the receiver operating characteristic curve is 0.9689 with (95% CI: 0.9308, 1) in case of no noisy labels for the classification of COVID-19 from heathy subject, while the per-exam sensitivity, specificity and accuracy for detecting COVID-19 in the independent testing set are 90.52%, 91.58% and 91.21%, respectively. In the classification of COVID-19 from other hybrid cases, the average AUC of the proposed model is 0.9222 with (95%CI: 0.8418, 1) if there are no noisy labels. The model is also robust when part of the training samples is marked incorrectly. The average AUC is 92.23% in the case of noisy labels of 10% in the training set. Conclusion A deep learning model with insufficient samples can be developed by using data augmentation in assisting medical workers in making quick and correct diagnosis of COVID-19.

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Section: Methodsmentioning

confidence: 99%

Automated Diagnosis of COVID-19 Using Deep Learning and Data Augmentation on Chest CT

Ruan

Xiang

et al. 2020

Preprint

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“…Such studies have been widely reported in the recent literature and use many data sources; they cover management, maintenance, safety and operations [141]. Image-processing approaches for implementing automatic detection have been suggested for monitoring railway infrastructure [128], rail track maintenance [133], railway track inspections and train component inspections [142]- [152] such as the rolling bearings of trains [153].…”

Section: Related Work In Railway Systemsmentioning

confidence: 99%

A Deep Learning Approach Towards Railway Safety Risk Assessment

Alawad

Kaewunruen

2020

IEEE Access

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Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks.

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“…However, training the model for diagnosing the faults requires the fault data under different loads. In the research of this paper, the generalization ability of the improved 1D-CNN under different loads was investigated and the result was compared to that of Shufflenet V2, MobileNet, ICN [29], DFCNN [30], and PFC-CNN [31]. The specific experimental contrast results are shown in Table 5 and Figure 13.…”

Section: Performances Under Different Loadsmentioning

confidence: 99%

An Improved of Fault Diagnosis Using 1D-Convolutional Neural Network Model

Chen¹,

Liu²,

Yang³

et al. 2020

Preprint

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The diagnosis of a rolling bearing for monitoring its status is critical for maintaining industrial equipment using rolling bearings. The traditional method of diagnosing faults of the rolling bearing has low identification accuracy, which needs artificial feature extraction to enhance the accuracy. 1D-CNN method not only can diagnose bearing faults accurately but also overcome shortcomings of the traditional methods. Different from machine learning and other deep learning models, the 1D-CNN method does not need pre-processing one-dimensional data of rolling bearing’s vibration. Thus, it enhances the processing speed and improves the network structure to have a reasonable design for small sample data sets. This study proposes and tests a 1D-CNN method for diagnosing rolling bearings. By introducing the dropout operation, the method obtains high accuracy and improves the generalizing ability. The experimental results show 99.52% of the average accuracy under a single load and 98.26% under different loads.

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Lightweight Convolutional Neural Network and Its Application in Rolling Bearing Fault Diagnosis under Variable Working Conditions

Cited by 46 publications

References 35 publications

Automated Diagnosis of COVID-19 Using Deep Learning and Data Augmentation on Chest CT

Automated Diagnosis of COVID-19 Using Deep Learning and Data Augmentation on Chest CT

A Deep Learning Approach Towards Railway Safety Risk Assessment

An Improved of Fault Diagnosis Using 1D-Convolutional Neural Network Model

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